def get_candidate_decomposed_clones(self, target_tumor, CluInf_tu,Tuseq):
Align = MegaAlignment()
NameOrder, Name2Seq = Align.name2seq(CluInf_tu[0])
LenSeq = len(Name2Seq[NameOrder[0]])
SigCluLs=[]
for Name in NameOrder: #Root is the first cluster or initial candidate clone
if Name!='#Clu0' and Name.find('Clu')!=-1: SigCluLs.append(Name)
CluCombLs,IDend=self.combinations([],SigCluLs,0,{})
print(target_tumor,'make cluster comb',SigCluLs,CluCombLs,NameOrder)
if CluCombLs!={}:
CloCan2Seq={}
Got_Candidate='n'
for Root in NameOrder: #Root is the first cluster or initial candidate clone
if Root=='#Clu0' or Root.find('Clu')==-1:
RootSeq=Name2Seq[Root]
if Root=='#Clu0': CloCan2Seq['#'+target_tumor+'Clu0']=RootSeq #Root is candidate clone
RootMut=Align.GetMutPos(RootSeq)
Got_Candidate='y'
if CluCombLs!={}:
for ID in CluCombLs:
CluLs=CluCombLs[ID]
CluN=''
MutPosLs=[]
for Clu in CluLs:
Seq=Name2Seq[Clu]
CluMut=Align.GetMutPos(Seq)
MutPosLs+= CluMut
CluN+=Clu.replace('#','')
Good='y'
for Mut in MutPosLs:
if RootMut.count(Mut)!=0: Good='n'
if Good=='y':
AllMutPosLs=MutPosLs+RootMut
Seq=Align.ModSeq('A'*LenSeq,AllMutPosLs,'T',LenSeq)
Redun_ls=Align.find_redundant(Seq,self.clone_seq) #all other clones ####
if Redun_ls==[]:
CloCan2Seq['#'+target_tumor+Root.replace('#','')+CluN]=Seq
if CloCan2Seq!={}:
CloCan2Seq.update(self.clone_seq)
Can_list=list(CloCan2Seq.keys())
new_seq = Align.UpMeg(CloCan2Seq,Can_list)
clone_frequency_combo = CloneFrequencyComputer_cnv1(new_seq, {target_tumor:self.v_obs[target_tumor]}, {target_tumor:self._CNV_file[target_tumor]}, self.freq_cutoff)
clone_frequency_combo.regress_cnv()
CluComboHit=self.findcombohit(clone_frequency_combo.hitclone_seq_builder)
if CluComboHit=='y':
print('test the quality of clustercombo, by removing tumor seq (if any)')
hit_seq_ls,hit_seq_dic=Align.name2seq(clone_frequency_combo.hitclone_seq_builder)
Tuseq_ls=Align.find_redundant(Tuseq,hit_seq_dic)
if Tuseq_ls==[]:
print('tumor genotype did not hit, so clustercombo is good')
return clone_frequency_combo.hitclone_seq_builder,Tuseq
else:
print('tumor genotype was hit, so test if clustercombo still hit without tumor genotype: testing if clustercombo genotypes fit well')
Tuseq_ls=Align.find_redundant(Tuseq,CloCan2Seq)
sub_hit_seq=[]
for seqname in CloCan2Seq:
if Tuseq_ls.count(seqname)==0:sub_hit_seq+=[seqname,CloCan2Seq[seqname]]
clone_frequency_combo_new = CloneFrequencyComputer_cnv1(sub_hit_seq, {target_tumor:self.v_obs[target_tumor]}, {target_tumor:self._CNV_file[target_tumor]}, self.freq_cutoff)
clone_frequency_combo_new.regress_cnv()
CluComboHit=self.findcombohit(clone_frequency_combo_new.hitclone_seq_builder)
if CluComboHit=='y':
return clone_frequency_combo_new.hitclone_seq_builder,Tuseq
else:
return CluInf_tu[0],''
else: return CluInf_tu[0] ,''
else: return CluInf_tu[0],''
return CluInf_tu[0],''
def BranchDecClone(self, seq_list, clone_frequency, Tu2CNV):
Align = MegaAlignment()
TumorSampleExtract = tsp_information(self.tsp_list)
CloFreAna = CloneFrequencyAnalizer()
CloOrder, Clo2Seq = Align.name2seq(seq_list)
Align.save_mega_alignment_to_file('Test.meg', seq_list)
tree_builder = MegaMP()
tree_builder.mao_file = self.mao_file
id = 'branchdec_mega_alignment'
status = tree_builder.do_mega_mp(seq_list, id)
if status == True:
seqs_with_ancestor, tree, nade_map, mask_seq, Good_posi_info = tree_builder.alignment_least_back_parallel_muts(
True
) # True will execute code to remove redundant seqs (True is default)
else:
print 'failed to run megaMP'
BadPosiLs = [] #multiple mutations
BadPosi2ChnageCloLs = {}
for c in Good_posi_info:
Posi_Inf = Good_posi_info[c]
if Posi_Inf != ['Good']:
if Posi_Inf[0] == 'ToWild':
BadPosiLs.append(c)
BadPosi2ChnageCloLs[c] = Posi_Inf[1][0]
print 'bad positions', BadPosiLs #,BadPosi2ChnageCloLs
if BadPosiLs != []:
NewT2C2F = {}
NewT2Cls = {}
for Tu in clone_frequency:
NewC2F = {}
single_tsp_list = TumorSampleExtract.make_single_tsp_list(Tu)
CloFreDic = clone_frequency[Tu]
CNV = Tu2CNV[Tu[2:]]
Tu = Tu[2:]
TuSeq = self.tumor2seq['#' + Tu]
NewCloLs = []
NewCloLs1 = []
for Clo in CloFreDic: #original hit clo for the tumor
ChangeOptions = 'n'
# print Tu,CloFreDic
if CloFreDic[Clo] > 0:
CSeq0 = Clo2Seq['#' + Clo]
ChangePosi = [] #list to fix multiple mutaitons
NewBadPosi = [
] #remove fixed multiple mutations from BadExtMutPosi
for Bad in BadPosi2ChnageCloLs:
if BadPosi2ChnageCloLs[Bad].count(
'#' + Clo) != 0 and (CNV[Bad] == 'normal'
or CNV[Bad]
== 'Bad-normal'):
Change = 'n'
for Oth in CloFreDic: #find multiple mutations at the external branch
if Oth != Clo and CloFreDic[Oth] > 0:
Soth = Clo2Seq['#' + Oth]
if Soth[Bad] == 'T' and BadPosi2ChnageCloLs[
Bad].count('#' + Oth) == 0:
Change = 'y'
if Change == 'y':
ChangePosi.append(Bad)
else:
NewBadPosi.append(Bad)
print 'change positions', Tu, ChangePosi
if ChangePosi != []: #fix multiple mutaitons
# print 'hhh'
CutCloSeq = Align.ModSeq(CSeq0, ChangePosi, 'A',
self.Len)
NewCloLs.append(Clo + 'Cut' + Tu)
NewC2F[Clo + 'Cut' + Tu] = CloFreDic[Clo]
Clo2Seq['#' + Clo + 'Cut' + Tu] = CutCloSeq
ChangeOptions = 'y'
if ChangeOptions == 'n':
NewC2F[Clo] = 1
NewT2C2F[Tu] = NewC2F
# print Clo2Seq
hitseq_align, hitclone_frequency = CloFreAna.ListHitCloAndSeq(
NewT2C2F, Clo2Seq)
outSeqMaj, outSeqAmb, NewT2C2F = Align.CombSimClo(
hitseq_align, hitclone_frequency, 0.0)
# print outSeqMaj, NewT2C2F
return outSeqMaj, NewT2C2F
else:
return seq_list, clone_frequency
def get_candidate_decomposed_clones(self, target_tumor):
Align = MegaAlignment()
CluInf_tu = self.ClusterInfo #[target_tumor]
NameOrder, Name2Seq = Align.name2seq(CluInf_tu[2])
# print target_tumor, CluInf_tu[0],CluInf_tu[1]
HitCloCluLs = CluInf_tu[1] #['T-'+target_tumor]
TuIdentical_seq = self.identical_seq['T-' + target_tumor]
LenSeq = len(Name2Seq[NameOrder[0]])
TuSeq = self.T2Seq['#' + target_tumor]
Clu2center = CluInf_tu[0]
SigCluLs = []
HitCloLs = []
HitCloSeq_dic = {}
RootClu = ''
LarCen = 0.0
for Hit in HitCloCluLs:
if HitCloCluLs[Hit] > 0.02:
if Hit[:len(target_tumor +
'Clu')] == target_tumor + 'Clu' and Hit.find(
'REP') == -1:
SigCluLs.append(Hit)
CluName = 'Clu' + Hit.split('Clu')[-1]
Center = float(Clu2center[CluName].split('-')[0])
for CluN in Clu2center:
Center2 = float(Clu2center[CluN].split('-')[0])
Sign = Clu2center[CluN].split('-')[1]
if Center == Center2 and CluName != CluN:
SigCluLs.append(target_tumor + CluN)
if LarCen < Center2: # and Sign=='Pos': #Pos for middle cut, Neg for K-means
LarCen = Center2
if Center == Center2: RootClu = target_tumor + CluN
elif LarCen <= Center2 and Sign == 'Pos': #Pos for middle cut, Neg for K-means
LarCen = Center2
if Center == Center2: RootClu = target_tumor + CluN
else:
HitCloLs.append(Hit)
HitCloSeq_dic['#' + Hit] = Name2Seq['#' + Hit]
# print 'cluls0',SigCluLs, HitCloLs, RootClu
if RootClu != '':
SigCluLs.remove(RootClu)
HitCloLs.append(RootClu)
# print 'cluls',SigCluLs, HitCloLs, RootClu
if SigCluLs != []:
CluCombLs, IDend = self.combinations([], SigCluLs, 0, {})
else:
CluCombLs = {}
# print CluCombLs
if RootClu != '' or CluCombLs != {}:
print 'make cluster comb'
CloCan2Seq = {}
Got_Candidate = 'n'
for Root in HitCloLs:
RootSeq = Name2Seq['#' + Root]
LenSeq = len(RootSeq)
RootMut = Align.GetMutPos(RootSeq)
CloCan2Seq['#' + Root] = RootSeq
Got_Candidate = 'y'
if CluCombLs != {}:
for ID in CluCombLs:
CluLs = CluCombLs[ID]
# print 'try make combo',Root,CluLs
CluN = ''
MutPosLs = []
for Clu in CluLs:
Seq = Name2Seq['#' + Clu]
CluMut = Align.GetMutPos(Seq)
MutPosLs += CluMut
CluN += Clu.replace(target_tumor + 'Clu', 'Clu')
MutPosLs = list(set(MutPosLs))
Go = 'y'
for Mut in MutPosLs:
if RootMut.count(Mut) != 0: Go = 'n'
if Go == 'y':
AllMutPosLs = MutPosLs + RootMut
Seq = Align.ModSeq('A' * LenSeq, AllMutPosLs, 'T',
LenSeq)
Redun_ls = Align.find_redundant(Seq, HitCloSeq_dic)
if Redun_ls == []:
CloCan2Seq['#' + target_tumor + Root.replace(
target_tumor + 'Clu', 'Clu') + CluN] = Seq
Got_Candidate = 'y'
if Got_Candidate == 'y':
Can_list = CloCan2Seq.keys()
# print 'find the good comb',Can_list
new_seq = Align.UpMeg(CloCan2Seq, Can_list)
alt_frequency = []
CNVls = self.CNV_info[target_tumor]
Len = len(CNVls)
c = 0
TuMatPosi = []
tumor_genotype = ''
while c < Len:
if CNVls[c] == 'normal':
alt_frequency.append(self.v_obs[target_tumor][c])
if self.v_obs[target_tumor][c] > 0:
TuMatPosi.append(c)
tumor_genotype += 'T'
else:
tumor_genotype += 'A'
c += 1
clone_frequency = CloneFrequencyComputer_cnv1({}, {}, {},
self.freq_cutoff,
{})
MutWildAlleleCount_noCNV = clone_frequency.make_mut_wild_allele_count_noCNV(
{}, Can_list,
CloCan2Seq) #PreAbsCNV, clone_order, SNV_seq, Tu2CloFre
Cmatrix_noCNV, Cmatrix_noCNV_dic = clone_frequency.make_Min(
Can_list, CloCan2Seq, MutWildAlleleCount_noCNV)
Clone2Freq = clone_frequency.do_nnls0(Cmatrix_noCNV, Can_list,
alt_frequency)
out2 = ['#MEGA', '!Title SNVs;', '!Format datatype=dna;', ' ']
AllMut = []
NewClone2Freq = {}
CluHit = 'n'
for Clo0 in Clone2Freq:
NewClone2Freq[Clo0] = Clone2Freq[Clo0]
if Clone2Freq[Clo0] > 0.02:
SeqMutPos = Align.GetMutPos(CloCan2Seq['#' + Clo0])
TuSeq = 'y'
for Mut in SeqMutPos:
if TuMatPosi.count(Mut) != 0: AllMut.append(Mut)
for Mut in TuMatPosi:
if SeqMutPos.count(Mut) == 0: TuSeq = 'n'
Iden = 'n'
for OriClo in self.OriAnc2Seq0:
c = 0
Dif = 'n'
while c < Len:
if self.OriAnc2Seq0[OriClo][c] != CloCan2Seq[
'#' + Clo0][c]:
Dif = 'y'
c += 1
if Dif == 'n': Iden = OriClo
if Iden != 'n':
out2 += [Iden, self.OriAnc2Seq0[Iden]]
NewClone2Freq[Iden[1:]] = Clone2Freq[Clo0]
NewClone2Freq[Clo0] = 0
elif TuSeq == 'n':
out2 += [
'#' + Clo0.replace(target_tumor + target_tumor,
target_tumor),
CloCan2Seq['#' + Clo0]
]
if Clo0.find('Clu') != -1 and Clo0.find(
'REP') == -1:
CluHit = 'y'
else:
out2 += [
'#' + target_tumor, CloCan2Seq['#' + Clo0]
]
NewClone2Freq[target_tumor] = Clone2Freq[Clo0]
NewClone2Freq[Clo0] = 0
AllMut = list(set(AllMut))
if len(AllMut) < len(TuMatPosi):
out2 += ['#' + target_tumor, tumor_genotype]
if CluHit == 'y':
# print 'Decomposed!' ,target_tumor,NewClone2Freq,out2
return out2, NewClone2Freq
return [], {}